The paper introduces the Research Progress Graph (RPG), a framework that captures the full, evidence‑driven evolution of research ideas through a closed‑loop process of experimentation, insight, challenge identification, literature exploration, and revision. Unlike current large‑language‑model agents that only record the final idea, the RPG records each research state, its temporal order, and the reasoning that links successive states, providing a complete, authentic trajectory. The authors compare thirty‑five existing agents to the RPG, showing that none fully preserves the evolving record, and argue that future agents should predict next research states, evaluate ideas as trajectories, and use authentic trajectories as training data.
arXiv:2610.07184v1 Announce Type: new Abstract: A key challenge in building AI systems for scientific research is enabling $\textit{scientific exploration}$: the systematic process of investigating u...
arXiv:2607. 10144v1 Announce Type: new Abstract: Scientific research is a complex, multi-stage workflow rather than a single act of text generation.
The paper introduces SciSense, a framework that structures scientific ideation into eight cognitive stages and a dataset, SciSense-Traj, of 120K citation-conditioned research trajectories in both hindsight and foresight modes. It presents SciSense-LM, a family of LLMs trained on these trajectories, showing that hindsight-trained models yield higher-quality, more novel outputs and improve downstream coding agents’ research artifacts. The study demonstrates that outcome-anchored ideation reduces cognitive load for downstream agents, enabling more creative exploration.
AutoResearch is a two‑stage autonomous research system that links Idea Generation with Idea Execution. In the generation phase it blends new research signals with existing domain knowledge, identifies transferable mechanistic insights, and produces grounded, testable research plans through multi‑model generation and cross‑review. The execution phase then decomposes these plans into experiments, iteratively implements and diagnoses them, and uses independent evidence‑based review to accept or revise conclusions, thereby turning ideas into measurable progress while minimizing hallucinations.
arXiv:2608. 10740v1 Announce Type: new Abstract: Effective research ideation requires moving beyond a static understanding of prior work to trace how research problems and solutions evolve across the literature.
arXiv:2608. 04738v1 Announce Type: new Abstract: Autonomous research agents can generate hypotheses, execute experiments, and draft manuscripts, yet their outputs often contain unsupported claims and inconsistencies between research questions, experiments, results, and conclusions.
arXiv:2606. 31229v1 Announce Type: new Abstract: Ideation plays a pivotal role in scientific discovery.
arXiv:2609.07611v1 Announce Type: new Abstract: Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Exi...
The paper reports on an autonomous research system that allows an AI agent to formulate its own research question, screen for failure modes, and obtain critiques from a separate model before writing a paper. Over 13 development runs, minor changes in the initial instruction document caused significant shifts in the topics explored, ultimately leading to a new technology applied to a societal problem. Five fully autonomous runs produced reproducible papers that were reviewed by external AI reviewers at workshop level, with all data and code released for replication.
The paper introduces SPRA (Structured Paper Representation for Agents), a comprehensive representation of AI research papers that organizes key research elements—Problems, Contributions, Components, ExperimentSetups, Measures, and Findings—into typed relations for easier querying, traversal, and reuse. SPRA includes paper-level citation relations that capture dependencies, component usage, and comparisons, and it has been instantiated across more than one million AI papers, yielding millions of research units and internal relations. Empirical evaluations show that SPRA improves source-conditioned scientific QA and directed cross-paper reasoning over passage retrieval while significantly reducing input and output token counts, demonstrating its practicality for scientific workflows.
arXiv:2609.27297v2 Announce Type: replace Abstract: Agentic science envisions many autonomous agents investigating concurrently while building on a shared, evolving body of scientific knowledge. This...
arXiv:2609.38445v1 Announce Type: new Abstract: Frontier LLMs are increasingly used to automate scientific research through iterative search. We distinguish idea-driven search from solution-driven se...